The present invention relates generally to airway segmentation, and more particularly to using 3-dimensional (3-D) morphology operators for airway detection and segmentation.
A Computed Tomography (CT) scan uses x-ray equipment to obtain image data from different angles around the human body and then uses computer processing of the information to produce cross-sectional images of the body tissues and organs. The image can then be analyzed by methods using morphological operators to highlight specific areas so that radiologists (or other medical personnel) can more easily diagnose problems associated with the patient such as cancers, cardiovascular disease, infectious disease, trauma and musculoskeletal disorders. Current high-resolution CT offers high resolution images of the chest and airways.
Virtual bronchoscopy (VB) systems are systems that evaluate an image of a patient's airways for the purposes of stenosis detection, stent design, guidance, or quantitative analysis. One basic precursor that can be vital to these systems is the segmentation of the airways. Segmentation of an airway is the isolation of the airway from the rest of an image. Segmentation offers the ability to later generate paths for guidance and forms a basis for quantitative measurements.
As shown in FIG. 1, the human airway tree 100 appears on a CT cross-section as a set of connected, dark, branching tubular structures that tend to decrease in diameter as the branching progresses.
Airway tree segmentation is traditionally a challenging problem. While airway voxels have a density near −1000 Hounsfield units (HU), noise and partial volume effects make it virtually impossible to use a simple threshold to identify all airway voxels within an image. Whenever mixtures of different tissue types comprise a voxel, intermediate gray-level values are the result. Voxels straddling air and airway walls typically have values above −1000 HU. These partial voxels can be resolved into tissue components through statistical methods. Moreover, due to the size of the voxel, thin or stenosed airways can appear broken or discontinuous. Finally, image reconstruction artifacts may cause the airways to appear discontinuous. Such discontinuities may cause problems during the segmentation, potentially resulting in both under- and oversegmentation errors.
Previously proposed airway segmentation methods have employed four strategies: (a) knowledge-based technologies, (b) region growing, (c) central-axis analysis, and (d) mathematical morphology. Hybrid algorithms combining two or more of these strategies also exist. Knowledge-based technologies describe structural relationships between airways and neighboring pulmonary vessels. Initially, 3-D seeded region growing is used to identify large airways. The knowledge-based rules are applied to the image on a section-by-section basis.
Region growing methods use voxel connectivity and a threshold to identify regions. In particular, region growing includes merging an initial set of points iteratively according to aggregation criteria. An arbitrary seed point is chosen and compared with neighboring points. A region is grown from the seed point by adding neighboring points that match aggregation criteria. This entire process is continued for each added point until no further additions are possible resulting in a single connected region.
Although 3-D region growing is typically extremely fast, it suffers from partial volume effects and noise due to the global threshold used during segmentation. The “optimal” thresholds differ for large versus small airways because of these factors. The resultant segmentation tends to lack finer details of the airways and contains rough edges. Further, these methods conventionally lose details, depict incomplete structures, or suffer from parenchymal leakage (“explosion”) to varying degrees.
Segmentation algorithms based on central axis analysis depend on central axis estimates for computing the segmentation. The disadvantage of this method is the critical dependence on the central axis analysis results, which may be imperfect or fail.
The field of mathematical morphology involves image-processing operations that focus on shape and gray-scale properties. Airway segmentation methods that draw on mathematical morphology tend to have two or more processing phases. First, candidate airways are detected by means of various morphologic operations. Next, 3-D relationships and shape characteristics help determine the true airways from false candidates. Typically, two-dimensional (2-D) operators of varying sizes are applied to each section of the image to identify candidate airways (i.e., candidates). Next, false candidates are eliminated through 3-D reconstruction. In one form of reconstruction, a region growing is performed on thresholded candidates with the seed point placed in the trachea. One potential pitfall with this approach is oversegmentation of the airways via false candidates. Further, most morphology-based methods require heavy computation times.